JACIII Vol.19 No.1 pp. 91-99
doi: 10.20965/jaciii.2015.p0091


Deep Level Emotion Understanding Using Customized Knowledge for Human-Robot Communication

Jesus Adrian Garcia Sanchez*, Kazuhiro Ohnishi*, Atsushi Shibata*,
Fangyan Dong**, and Kaoru Hirota*

*Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

**Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, J3-141, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8501, Japan

April 30, 2014
September 2, 2014
January 20, 2015
emotion understanding, human-robot communication, multi agent, kinect sensor
In this study, a method for acquiring deep level emotion understanding is proposed to facilitate better human-robot communication, where customized learning knowledge of an observed agent (human or robot) is used with the observed input information from a Kinect sensor device. It aims to obtain agentdependent emotion understanding by utilizing special customized knowledge of the agent rather than ordinary surface level emotion understanding that uses visual/acoustic/distance information without any customized knowledge. In the experiment employing special demonstration scenarios where a company employee’s emotion is understood by a secretary eye robot equipped with a Kinect sensor device, it is confirmed that the proposed method provides deep level emotion understanding that is different from ordinary surface level emotion understanding. The proposal is being planned to be applied to a part of the emotion understanding module in the demonstration experiments of an ongoing robotics research project titled “Multi-Agent Fuzzy Atmosfield.”
Cite this article as:
J. Sanchez, K. Ohnishi, A. Shibata, F. Dong, and K. Hirota, “Deep Level Emotion Understanding Using Customized Knowledge for Human-Robot Communication,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.1, pp. 91-99, 2015.
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